1. Field of the Invention
The present invention is generally directed to bearing measuring sensors. More particularly, the present invention is directed to methods and systems for providing at least one clustered network of bearing measuring sensors. The invention is particularly useful in utilizing at least one clustered acoustic bearing sensor network for estimating a target location. For example, the present invention may be used for estimating a target location of a ground or an airborne asset. However, aspects of the invention may be equally applicable in other scenarios as well.
2. Description of Related Art
Acoustic sensing technology can be used effectively by the various armed services of the United States of America for detecting and tracking ground and airborne assets. For example, acoustic sensing technology allows the acoustic sound emitted by ground vehicles, helicopters, aircraft and the like to be passively detected without certain line-of-sight restrictions. Such line-of-site restrictions may arise with other conventional tracking systems, such as radar and/or optical systems. Acoustic sensing technology may also be employed for a variety of civilian uses as well. For example, one such application could involve providing a low-cost, passive, aircraft tracking capability for small airports where radars are not practical and/or cost effective.
As is generally known, acoustic bearing sensors measure a bearing angle from a sensor to a target using an array of three or more microphones. An acoustic bearing sensor generally refers to an assembly comprising an array of microphones. Theoretically, once two sensors determine a bearing angle to a target, geometric triangulation can be used to determine the target location. For example, FIG. 1 illustrates a conventional two sensor arrangement 10. As shown in FIG. 1, two bearing sensors 12, 14 are provided in a conventional sensor arrangement and are arranged to establish a target location of target 16. This arrangement 10 utilizes a triangulation method to derive target location information. Triangulation in this simple two-sensor case consists of determining the intersection of the two bearing lines. The estimated location of the target is at the intersection.
A typical acoustic bearing sensor determines the bearing (or azimuth) to target by analyzing acoustic sound waves 18 that target 16 emits. Such acoustic sound waves 18 could include an engine noise, a muffler noise, a tire-road noise, or other like acoustic sounds. Muffler noise is a dominant noise that is typically detected in most situations. Therefore, particular acoustic sensors are used to track or locate targets having engines (and/or wheels) such as trucks, tanks, airborne crafts such as helicopters and airplanes, and the like.
One concern with acoustic bearing sensors is that they possess a random bearing angle measurement error and therefore cannot be used with the simple triangulation method as illustrated in FIG. 1. Such a random bearing angle measurement error could have a standard deviation of three degrees (i.e., 3°) or more and this standard deviation can cause an uncertainty area when determining a location of a target, such as target 16. Consequently, accurate generation of target location information is difficult.
FIG. 2 illustrates an arrangement 20 of two sensors 22, 24 having a potential uncertainty area 19 in determining the location of target 16 based on acoustic sound waves 18. The uncertainty area is due to an uncertainty of the bearing direction estimates of the two sensors. The two bearing lines originating from each sensor define the uncertainty of the bearing angle estimate. The correct bearing directions are somewhere in between the two lines. When the two bearing lines from each sensor intersect the two bearing lines from the other sensor, they define a quadrilateral (a polygon with 4 sides) which represents the uncertainty area 19 of the target location estimate. While the target 16 is at a specific location, the triangulation of the bearing estimates of the sensors 22, 24 would estimate the target location at a random point that could be anywhere within the uncertainty area 19.
Consequently, depending on a distance between sensor and target, a large and therefore unacceptable uncertainty area 19 may be present. As just one example, in the event that an acoustic sensor is positioned at a distance of 500 meters from target 16, the three degree angular error translates into a linear target location error with a standard deviation (σ) of 500 meters×tan(3°), or approximately 26.2 meters. Assuming that a random variable can frequently reach its ±3σ values, a linear uncertainty such as uncertainty area 19 can approach ±3×26.2 m=±78.6 meters in magnitude. Such a linear uncertainty on the order of almost 80 meters is considered too large of an uncertainty for certain applications, such as most target tracking applications since there may be a desire to intercept a target with a weapon and the weapon will miss the target if the target is substantially smaller than this uncertainty area. These error estimates are derived assuming optimal sensor geometry where the two bearing lines intersect perpendicularly. Sub-optimal sensor geometry results in even larger linear errors, as do longer sensor-to-target distances. When the two bearing lines intersect approximately perpendicularly, as in FIG. 2, the uncertainty area is a quadrilateral (polygon with 4 sides) that is close in shape to a square and the errors are approximately equal in all directions. When the bearing lines intersect with a small angle, such as on the order of 10°, the uncertainty area becomes a very long and thin quadrilateral. Consequently, the errors in the direction where the uncertainty area is very long can be quite large, for example, several times the ±78.6 meter error estimate from above.
One method of overcoming this concern of large location estimation errors is to estimate a target location using a plurality of bearing sensors. For example, in one arrangement, a plurality of sensors on the order of hundreds or even thousands of bearing sensors may be networked together to produce a global target location estimate, that is, a target location estimate generated by input from many bearing sensors. Using such a large quantity of bearing sensors with each bearing sensor having an independent random bearing error will average out these random errors and therefore lead to a more accurate global target location estimate.
For example, FIG. 3 illustrates a bearing sensor network 50 comprising a single cluster 68 comprising a plurality of bearing sensors. In this arrangement, the triangulation method of FIG. 2 is replaced with an optimization that can use a large number of sensors. More particularly, network 68 comprises six sensors 52, 54, 56, 58, 60, and 62 and these sensors are all operatively coupled to a central computing node 70. Network 50 of six bearing sensors 52, 54, 56, 58, 60, and 62 illustrated in FIG. 3 could include hundreds or even thousands of sensors. This network could also be disbursed over a large land mass, for example, a land mass on the order of tens or even hundreds of square kilometers.
One approach to locating target 64 in FIG. 3 is to utilize an expanded sensor network and transmit a bearing measurement of each sensor 52, 54, 56, 58, 60, and 62 to a central computing node 70. Central computing node 70 then gathers bearing measurements generated by each of the plurality of bearing sensors 52, 54, 56, 58, 60, and 62 and utilizes a global optimization to estimate a global target location estimate of target 64. In the absence of a global optimization, each pair of bearing sensors in FIG. 3 would estimate a different target location based on triangulation of their two bearing lines. The intersections of these pairs of bearing lines are marked by the circles in FIG. 3. However, it is preferred to estimate one globally optimal location of the target that is based on the measurements of all the sensors rather than computing multiple estimates based on each pair of sensors. The one globally-optimal location is the most accurate estimate that can be derived based on the mathematics and physics of this target location problem.
Typically, sensors measure bearing angles about once per second and target location estimates are computed at the same rate (i.e, 60 estimates per minute). Therefore, a centralized sensor network control approach requires high communication bandwidth so that each sensor in the network can transmit wirelessly its bearing estimate to the central computing node 70 once per second. It is likely that this would result in wireless communication bottlenecks and not all sensor measurements would reach central computing node 70 in time for the once-per-second computation of the target location. Such a centralized sensor network approach would also be more prone to failures when sensors or entire regions of the sensor network fail because the central computing node 70 would have to resolve these problems while possibly located many kilometers away from the location of the problem.
There is, therefore, a general need for a method and/or system that provides a means of optimally estimating a target location using bearing measurements, preferably from a plurality of sensors. There is a further need to provide a means of optimally estimating a target location using bearing measurements from a plurality of sensors while minimizing the required communication within the network so that the information from all the sensors can reach the central computing node in time for target location computation. There is a further need to make the sensor network more robust to local sensor failures and to communication failures by making the network less centralized, i.e., by managing clusters of sensors locally and allowing the central computing node to deal with a limited number of clusters rather than to deal individually with every single sensor in the network.